Facilitation of the Distribution of Scientific Data

ABSTRACT

A technology that facilitates distribution of scientific data are disclosed. Exemplary implementations may: obtain scientific data from sci-data gathering devices; analyze gathered sci-data to identify and categorize relevant scientific elements in the gathered sci-data; offer the analyzed sci-data via a marketplace in exchange for a monetary value; and deliver the analyzed sci-data via the marketplace in exchange for an acceptable monetary value.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication No. 62/644,331, filed Mar. 16, 2018, entitled DataCollection and Analytics Based on In-Situ Biological Cell Detection andIdentification, U.S. Provisional Patent Application No. 62/549,543,filed Aug. 24, 2017, entitled In-The-Field Pathobiological CellDetection, U.S. Provisional Patent Application No. 62/588,754, filedNov. 30, 2017, entitled Data Collection and Analytics Based on In-SituBiological Cell Detection and Identification, U.S. Provisional PatentApplication No. 62/797,703, filed Jan. 28, 2019, entitled AmeliorationBased on Detection of Biological Cells or Biological Substances, PCTPatent Application No. PCT/US18/47568, filed Aug. 22, 2018, entitledDetection of Biological Cells or Biological Substances, and PCT PatentApplication No. PCT/US18/61841, filed Nov. 19, 2018, entitled DataCollection and Analytics Based on Detection of Biological Cells orBiological Substances, the contents of which are incorporated herein byreference in their entirety.

BACKGROUND

Finding a cure for cancer. Preventing the spread of infectious disease.Defeating superbugs. These are some of the most critical challengesfacing our world today, and we are losing ground on each of them.

Today, our approach to fighting global health threats is not working.Many dangerous illnesses still lack efficient prevention or treatmentoptions and continue to affect millions of people every day. Globally,$7 trillion each year is spent on healthcare. Considering that 90% ofclinical trials end in failure, and a single clinical trial can costbetween $800 million and $1.4 billion, there is a major imbalance in thesystem.

In the last century, a number of scientific breakthroughs havefundamentally improved global health. Today, people live longer andhealthier than ever before. Many diseases that were once fatal ordebilitating can now be prevented or treated due to monumentalbreakthroughs in areas like antibiotics and vaccines. Infections thatwere once the leading causes of mortality have been largely eliminated.Vaccines have led to the eradication or control of many devastatinginfectious diseases, including polio, smallpox, diphtheria and measles.Even diseases that still need our attention, like HIV, can beeffectively controlled through multi-drug regimens that preventprogression.

Despite these major breakthroughs, many of the most common diseases arenot effectively treated by existing therapies. Lung, colon, breast andprostate cancers are all still incurable once they have metastasized.The vast majority of orphan diseases, including rare cancers, lackeffective therapies. Heart disease and stroke remain leading causes ofmortality, while treatments for psychiatric diseases remain elusive. Wehave also seen the advent of entirely new issues that we areill-equipped to tackle, including antibiotic-resistant bacteria (or“superbugs”) and large-scale infectious disease prevention for illnesseslike Ebola or H1N1.

The world has outgrown the system that has worked so well in the past,and it is time to adapt to our present challenges by creating anentirely new system. New threats demand new solutions.

Before a clinical trial or research project can begin, it requires anenormous amount of funding. Because the entities that have access tofunds include major institutions, governments and pharmaceuticalcompanies, these are the groups that end up determining researchpriorities by choosing which ones to pursue. Many times, thesepriorities don't align with actual global health threats, but ratherwith the best interests of the institution or are in response to amarket size that would make a certain drug lucrative.

Today, there are significant borders between institutions andgovernments that mean important research is siloed instead of beingwidely accessible to all. A group of researchers working to understand acomplicated protein as part of a cancer treatment research project inAustralia might have no idea that a scientist in Germany has alreadydone the legwork that they need. Meanwhile, that German researcher mighthave no idea that her protein research could be a critical component tothe development of an important cancer treatment. Essentially,scientists around the world don't presently have all the puzzle piecesthey need to solve these grand human health challenges. The current peerreview process and communication structures in the scientific communitymake it very difficult for researchers to connect with one another andimprove upon each other's work, making it impossible to make realprogress toward any of our important, collective goals in global health.

Globally, Clinical trials can cost between $800 million and $1.4 billioneach to conduct and have an exceptionally high (90%) failure rate.Clearly, the way clinical trials are conducted today is economicallyinefficient, but the rate of failure also speaks to inefficiencies inour scientific approach.

In a clinical trial to test a potential new drug, for example,scientists start by studying their drug candidate in a sterile, isolatedlaboratory environment. Drug candidates often appear to work well inthese pre-clinical trials, where they are studied in test tubes, Petridishes or animals like mice. When the drug candidate is moved into humantrials, the story changes.

Because the human body is highly complex, it makes sense that a drugcandidate would act differently in a human than it would in anartificial, controlled setting. To take the guesswork out of clinicaltrials and make important discoveries, what is needed is a new systemthat gives a better understanding of how molecules act in the realworld, not just in laboratories, to give scientists a higher chance ofsuccess.

SUMMARY

One aspect of the present disclosure relates to a system configured thatfacilitates distribution of scientific data. The system may include oneor more hardware processors configured by machine-readable instructions.The processor(s) may be configured to obtain scientific data fromsci-data gathering devices. The processor(s) may be configured toanalyze gathered sci-data to identify and categorize relevant scientificelements in the gathered sci-data. The processor(s) may be configured tooffer the analyzed sci-data via a marketplace in exchange for a monetaryvalue. The processor(s) may be configured to deliver the analyzedsci-data via the marketplace in exchange for an acceptable monetaryvalue.

Another aspect of the present disclosure relates to a method thatfacilitates distribution of scientific data. The method may includeobtaining scientific data from sci-data gathering devices. The methodmay include analyzing gathered sci-data to identify and categorizerelevant scientific elements in the gathered sci-data. The method mayinclude offering the analyzed sci-data via a marketplace in exchange fora monetary value. The method may include delivering the analyzedsci-data via the marketplace in exchange for an acceptable monetaryvalue.

Yet another aspect of the present disclosure relates to a non-transientcomputer-readable storage medium having instructions embodied thereon,the instructions being executable by one or more processors to perform amethod that facilitates distribution of scientific data. The method mayinclude obtaining scientific data from sci-data gathering devices. Themethod may include analyzing gathered sci-data to identify andcategorize relevant scientific elements in the gathered sci-data. Themethod may include offering the analyzed sci-data via a marketplace inexchange for a monetary value. The method may include delivering theanalyzed sci-data via the marketplace in exchange for an acceptablemonetary value.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of ‘a’, ‘an’,and ‘the’ include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates conventional techniques to detect and identifypathogens.

FIG. 2 illustrates an example system in accordance with the presentdisclosure.

FIG. 3 illustrates a chip architecture in accordance with the presentdisclosure.

FIG. 4 illustrates an example system in accordance with the presentdisclosure.

FIG. 5 is a flow chart illustrating an example method in accordance withthe present disclosure.

FIG. 6 illustrates an example of platform architecture in accordancewith the present disclosure.

FIG. 7 illustrates an example system in accordance with the presentdisclosure.

FIG. 8 illustrates a system configured that facilitates distribution ofscientific data, in accordance with one or more implementations.

FIGS. 9A and/or 9B illustrates a method that facilitates distribution ofscientific data, in accordance with one or more implementations.

The Detailed Description references the accompanying figures. In thefigures, the left-most digit(s) of a reference number identifies thefigure in which the reference number first appears. The same numbers areused throughout the drawings to reference like features and components.

DETAILED DESCRIPTION

A technology that facilitates distribution of scientific data aredisclosed. Exemplary implementations may: obtain scientific data fromsci-data gathering devices; analyze gathered sci-data to identify andcategorize relevant scientific elements in the gathered sci-data; offerthe analyzed sci-data via a marketplace in exchange for a monetaryvalue; and deliver the analyzed sci-data via the marketplace in exchangefor an acceptable monetary value.

Disclosed herein is technology to accelerate the development of curesfor global health threats, including preventive treatments,therapeutics, vaccines and drug development. Taking creative advantageof cutting-edge technologies, a secure, borderless economy is createdthat mints its own currency to incentivize data collection and empowerevery organization, government, professional scientist or global citizenscientist to participate. The model is anchored by a platform, whichincludes a microprocessor chip to be used in various devices, such asInternet of Things (IoT) develops. The chip may be based on variousarchitectures, such as RISC architectures, for example Arm SemiconductorCompany based architectures. The chip can collect molecular data in realtime, increasing the amount of available data by orders of magnitude. Itis to be understood that platform and system are interchangeableterminology. The environment can include the platform or system, and amarketplace that are further described below.

Furthermore, blockchain technology may be used along with cryptocurrencyor a Coin, to secure and authenticate data, attribute each piece of datato its correct source and compensate participants for theircontributions.

With blockchain technology and the Coin serving as the foundation, theeconomy can include the following four fundamental concepts:

1) Aggregation of research-based data from organizations andinstitutions on a global scale;

2) The collection of real-time, real-world molecular level data from ourdevices implementing the chip and Internet of Things (IoT) devices;

3) The application of artificial intelligence and machine learningsystems to identify trends and draw conclusions, providing scientistsand researchers a tool to create life-saving solutions;

4) Inclusion of everyone on a global scale, from the sick or threatenedto the research scientist or citizen scientist.

Example Scenario

Listeria monocytogenes is a pathogen that causes listeriosis, which isan infection with symptoms of fever, vomiting, and diarrhea. Thispathogen is an example of a pathobiological cell. Listeria can spread toother parts of the body and lead to more serious complications, likemeningitis. Listeria is often transmitted by ready-to-eat foods, such asmilk, cheese, vegetables, raw and smoked fish, meat, ice cream, and coldcuts. This early and rapid detection is desirable so thatcross-contamination can be avoided and any problems immediatelyaddressed.

These ready-to-eat foods are often mass produced in food factories. Inthese factories, there is little to no time to stop production to testto determine if a harmful pathogen (like listeria) exists on thefood-production surfaces. Depending on the comprehensiveness and desiredaccuracy of the test, conventional techniques to detect the bacteriatake as long as a week to as short as hours. Regardless of theparticulars of the test, these conventional tests involve the manualcollection of samples from various surfaces, cataloging these samples,and performing invasive testing (e.g., culturing, chemical reaction,antibodies, etc.).

FIG. 1 illustrates conventional techniques 100 to detect and identifypathogens. Table 110 has a surface that, of course, has microbesthereon. This table 110 represents anything with a surface area thatmight have microbes living on it. For this discussion, assume that table110 is in a commercial kitchen of a ready-to-eat food manufacturer. Thismanufacturer is concerned about Listeria in this kitchen. To detect theexistence of Listeria in its kitchen, the manufacturer orders spot testsbe performed in the kitchens.

To that end, a spot 112 on table 110 is selected for sampling. Using asample-collection swab 120, a tester swipes the spot 112. Followingarrow 130, a sample-collected swab 122 is carefully transported to atesting station 140 so as to avoid collateral and inadvertent collectionof samples from other sources.

Typically, this testing station 140 is physically separated and distantfrom the sample spot 112 of the commercial kitchen where the sample wascollected. The testing station 140 is often in a laboratory of a testingfacility. With traditional methods, the sample microbes 124 of thesample-collected swab 122 are transferred to Petri dishes 144 forcultivation. At some point, chemicals 142 may be added to the culturedmicrobes of the Petri dishes 144 for various reasons, such as dyes tomake them more visible.

Following arrows 132 and 134 and perhaps weeks or months, a petri dish146 with the adultered (e.g., dyed) cultured microbes is ready to beexamined under a microscope 150. Typically, a human examines thecultures under the microscope 150 and identifies pathogens amongst thecultured microbes based on many factors, but mostly the human'sprofessional experience in identifying such microbes.

Traditional methods of testing like that demonstrated in FIG. 1, wheresample microbes are cultivated in labs, are flawed. ‘Stressed’ cellswill not grow in cultures (and will, therefore, produce negativeresults) despite the bacteria being present, live and potentiallyharmful. Also, this is the slowest form of testing.

Alternative conventional techniques, based on molecular/chemicalmethods, detect all cell types, but don't differentiate between live andharmless dead cells, which can remain after disinfection. Thus, thesemolecular/chemical methods may indicate a false positive for thepresence of a pathogen when only dead cells of the pathogen are present.

Still, other conventional techniques use antibodies to test biofilms,which are groups of microbes where cells stick together on a surface.This technique requires the biofilms to be removed from the surface,treated with a particular antibody, and then tested to see if thebiofilm fluoresces. This type of technique only tests for the particularpathogen that the introduced antibody interacts with.

Example Electronic Device

FIG. 2 illustrates an example scenario 200 in accordance with thetechnology described herein. The example scenario 200 includes a table210. That table has a scene 212, which is an area of a surface in viewof a camera (not shown) of a smartphone 220. Indeed, the camera capturesan image 222 of scene 212. Just like reality, the scene 212 includesmultiple microbes, but these microbes are not visible in scene 212.

For the example scenario 200, the microbes are described as in situ(i.e., in place) because they are examined, tested, etc. where theynaturally live, inhabit, or exist. That is, the microbes are not in thelab. Herein, “in the lab” indicates that the microbes have been moved,relocated, or expatriated in order to perform the examination, testing,or the like. Other implementations of the technology described hereinmay involve microbes that are in the lab.

That image 222 is depicted as being viewed on a display of thesmartphone 220. The image 222 has been sufficiently magnified to be ableto see various in situ microbes of scene 212. And while not yetdetected, one of these microbes is a pathogen 224.

The smartphone 220 is one example of an electronic device 230 inaccordance with the technologies described herein. However, in otherexample scenarios, the electronic device 230 may be, for example, atablet computer, a smartdevice, a standalone device, a collection ofcooperative devices, a button-sized device, a device on a chip, anaccessory to a smartphone or smartdevice, an ambulatory device, a robot,swallowable device, an injectable device, embedded within medical labequipment, or the like.

As depicted, the electronic device 230 includes a scene-capture system,a biologic detection system (and object detection system) 234, adatabase 236, an environmental sensor system 238, a report system 240,and an amelioration system 242. These systems of the electronic device230 are constructed from hardware, firmware, special-purpose components(e.g., sensors), and/or some combination thereof. These systems may, insome instances, include software modules as well.

The scene-capture system 232 is designed to obtain an image (e.g., image222) of a scene (e.g., scene 212) that includes in situ biological cellstherein. That is, there are biological cells located in a place (i.e.,in-the-field) in the scene that is being captured by the scene-capturesystem. In some implementations, the scene-capture system 232 includes acamera to capture the visible part of the electromagnetic spectrum thatis emitting or reflecting from the matter contained in the scene. Insome implementations, the scene-capture system 232 includes componentsdesigned to capture non-visible parts of the electromagnetic spectrum(e.g., x-rays, infrared, gamma rays, ultraviolet, etc.) that is emittingor reflecting from the matter contained in the scene.

Examples of the action of obtaining (as performed by the scene-capturesystem 232) include measuring, collecting, accessing, capturing,procuring, acquiring, and observing. For example, the scene-capturesystem 232 may obtain the image by capturing the image using thecharge-coupled device (CCD) of the digital camera. In another example,the scene-capturing system 232 may obtain the image by measuring theelectromagnetic spectrum of the scene.

The obtained image is micrographic, spectrographic, digital, or somecombination thereof. The obtained image is micrographic because itcaptures the elements in the scene that are on a microscopic scale. Theobtained image is spectrographic because it captures elements in thescene by using equipment sensitive to portions of the electromagneticspectrum (visible and/or non-visible portions). The obtained image isdigital because it formats and stores the captured information as datacapable of being stored in a machine, computer, digital electronicdevice, or the like.

While the thing that is captured is called an image, this image is notnecessarily displayable as a two-dimensional depiction on a displayscreen (as shown in image 222). Rather, the image an array of data thatrepresents the quantitative and qualitative nature of theelectromagnetic spectrum (or some portion thereof) received by thecomponents of the scene-capture system 232 when it was exposed to thescene (e.g., scene 212).

The biologic detection system 234 is designed to analyze the obtainedimage and detect the presence of one or more pathobiological cellsamongst the in situ biological cells of the captured scene. In someimplementations, the biologic detection system 234 may actually identifyone or more particular cells and/or substances in the scene. In thatcase, it may be called a biologic identification system. Such a systemmay identify the particular pathobiological cells amongst the in situbiological cells. Thus, depending on the implementation, this system 234may be referred to as biological-cell detection system or thepathobiological detection system.

To accomplish detection, the biologic detection system 234 may employ onand/or employ a database 236. This database 236 may be a database ofpathobiologic-cellular signatures or training corpus. The biologicdetection system 234 is a particular example of a biologic-cellulardetection system. A training corpus is a database of numerousapplication-specific samples from which the AI/ML/DL engine “learns” andimproves its capabilities and accuracy.

The biologic detection system 234 employs an AI/ML/DL engine to performor assist in the performance of the detection and/or identification ofone or more pathobiological cells. AI/ML/DL is short for artificialintelligence/machine learning/deep learning technology. Particularimplementations may employ just an AI engine, just an ML engine, just aDL engine, or some combination thereof.

The AI/ML/DL engine may be implemented just on the smartphone 220itself. In that case, the smartphone 220 need not communicate in realtime with the platform (e.g., a remote computing system). In anotherimplementation, the AI/ML/DL engine may be implemented just on theplatform (thus remotely). In that case, the smartphone 220 communicatesin real time (or nearly so) with the platform (e.g., a remote computingsystem). In still other implementations, the AI/ML/DL engine isimplemented partially in both the smartphone 220 and the platform. Inthis way, the intensive processing is offloaded to the platform.

Some implementations of the biologic detection system 234 may performits data analysis solely on the device without assistance from otherdevices, servers, or the cloud. Other implementations of the biologicdetection system 234 may farm out all or nearly all of the data analysisto other devices, servers, or the cloud. In still other implementations,the data analysis may be shared amongst multiple devices and locations.

On its own or working with other devices or computer systems, thebiologic detection system 234 analyzes the image of the scene to detect,determine, and/or identify the type or class of biological cellstherein. It may do this, at least in part, by using distinguishingmolecules of the cells that are observable using the electromagneticspectrum. Is some implementations, other data (such as chemicalreactions or excitation) may be included in the analysis.

Some of these molecules are indicative of certain classes, types, orparticular cells. Such molecules are called marker biomolecules herein.The electronic device can determine which cell types or class arepresent in a captured scene by the on the particular ones of, the typesof, and the proportions of the biomolecules detected therein. This maybe accomplished, at least in part, by calculating probabilities ofobjects detected in the image.

The environmental sensor system 238 is designed to measure one or moreenvironmental factors associated with the in situ biological cells orthe environment surrounding the in situ biological cells. In someinstances, the environmental sensor system 238 may be simply describedas a sensor.

The report system 240 is designed to report detection and/oridentification of the one or more pathobiological cells in the obtainedimage and in some implementations, the report system 240 is designed toassociate the measured environmental factor with the obtained imageand/or with the detected pathobiological cell.

The amelioration system 242 is designed to respond to the detectionand/or identification in a manner that ameliorates the pathobiologicalnature of the detected/identified pathobiological cells.

The electronic device 230 may have a communications system to send orreceive data from other similar electronic devices orcentralized/distributed servers. The electronic device 230 may haveenhanced processor or co-processor to perform image-capture andprocessing functions.

Of course, the example scenario 200 described above is oneimplementation that detects pathobiological cells. Other implementationsof the technology described herein may detect or identifypathobiological substances rather than cells.

One or more of the systems of electronic device 230 may be characterizedas a non-transitory computer-readable storage medium comprisinginstructions that when executed cause one or more processors of acomputing device to perform the operations of that electronic device.

Pre-Processed Data

Typically, images of the same scene are captured overtime. That may beover a few seconds, few minutes, or perhaps a few hours. In so doing, amassive amount of raw image data is produced. So much data that it mayquickly overwhelm the local storage capacity of the smartphone 220 andoften overwhelms the data transfer rate between the smartphone 220 andany network-based storage solution.

To address this issue, the smartphone 220 maybe designed to pre-processor process the raw scene-captured data before storing locally ortransferring it across the network. For pre-processing, the smartphone220 may derive just the most critical or key data that helps identity orreconstruct the scene.

For example, the smartphone 220 may store the marker biomoleculeinformation from each captured image or scene. The marker biomoleculeinformation includes just the data regarding the type, amount,proportions, etc. of the marker biomolecules or substances detected,determined, identified, etc. in a particular image or scene. Thus, anyother data from the image capture is discarded.

Along with associated environmental factors, this pre-processedinformation is stored or transferred across the network. This reducesthe data storage/transfer requirements by multiple orders of magnitude.The particular cell type or class is determined from this stored ortransferred pre-processed data. This may be done later or by differentsystems.

In some instances, the smartphone 220 may fully process the image/scenecaptured data to determine, detect, and/or identify the cell type orclass. In this scenario, the electronic device stores or transfers itsconclusion about the cell type or class with its associatedenvironmental factors.

Scale of Amount of Data

Since the images being captured are on a microscopic scale, it may takemany images to capture a small surface area of an object. In addition,even a short sequence of images adds up quickly to a great multitude ofimages. Thus, in only a short space (e.g., of just a few seconds), thesequence of microscopic-scale images of a very small area quicklyoverwhelms the internal data transfer, data storage, and/or dataprocessing capability of a typical electronic device (such as asmartphone). In addition, the typical external data transfer rates(e.g., of wireless communication) is not capable of accepting the datatsunami of this technology.

Two example approaches may be employed to address these issues. Oneinvolves the increased capacity of the electronic device, and the otherinvolves the processing of the data into a manageable form.

First, this technology is implemented in such a way to employspecial-purpose hardware to perform the pre-processing and processing ofthe incoming real-time data. That is, the specially designed hardware isbuilt directly into the processors and electronics of the device toenable the device to quickly process the massive amount of incomingreal-time data into a representative portion thereof without losingimportant aspects of the data.

Second, this technology employs a particular mechanism to produce arepresentative portion thereof without losing important aspects of thedata. In short, that involves saving the deltas (i.e., changes) betweenthe measurements (e.g., marker biomolecules) over time. These deltas arestored and/or transferred. In addition, data compression schemes may beused.

Imaging

The technology described herein utilizes an image-capturing system, suchas the scene-capture system 232. In some instances, the image-capturingsystem may be called a camera. This is particular so when the systemcaptures the visible part of the electromagnetic spectrum that isemitting and/or reflecting from the matter being observed. In someimplementations, the image-capturing system may capture non-visibleparts of the electromagnetic spectrum (e.g., x-rays, gamma rays,ultraviolet, etc.) that are emitting or reflecting from the matter beingobserved.

With some implementations, the image-capturing system may employhyperspectral imaging and, in particular, snapshot hyperspectralimaging. Hyperspectral imaging collects and processes information fromacross a portion of the electromagnetic spectrum. With hyperspectralimaging, the spectrum is captured for each pixel in the image of ascene. Snapshot hyperspectral imaging uses a staring array (rather thana scanning array) to generate an image in an instant.

With some implementations, the image-capturing system may employlight-field imaging, which is also called plenoptic imaging. A plenopticimaging system captures information about the light field emanating froma scene. That is, it captures the intensity of light in a scene, andalso the direction that the light rays are traveling in space. Thiscontrasts with a conventional camera, which records only lightintensity.

Using plenoptic imaging enables the simultaneous capture of pictures atdifferent focal points, allowing the device sensor to capture atwo-dimensional image in multiple 3rd dimension planes (i.e., capture avolume of space vs. a plane). Capturing a volume facilitates fasterdetection on non-flat surfaces or when fluids or gases are observed.

In addition, a combination of both these hyperspectral and plenoptictechnologies may be used. That is, the image-capture system mayincorporate both snapshot hyperspectral imaging with plenoptic imaging.

Nanophotonics

In some instances, an agent is purposefully introduced into the scene,environment, or in the lab to enhance or improve the observations ormeasurements. For example, photonic nanostructures may be spread in theenvironment where measurements and observations may be made.

These photonic nanostructures are part of a field called nanophotonicsor nano-optics, which involve is the study of the behavior of light onthe nanometer scale, and of the interaction of nanometer-scale objectswith light. It is a branch of optics, optical engineering, electricalengineering, and nanotechnology. It often (but not exclusively) involvesmetallic components, which can transport and focus light via surfaceplasmon polaritons.

The term “nano-optics,” just like the term “optics,” usually refers tosituations involving ultraviolet, visible, and near-infrared light(free-space wavelengths from 300 to 1200 nanometers).

Using nanophotonics to create high peak intensities: If a given amountof light energy is squeezed into a smaller and smaller volume(“hot-spot”), the intensity in the hot-spot gets larger and larger. Thisis especially helpful in nonlinear optics; an example issurface-enhanced Raman scattering. It also allows sensitive spectroscopymeasurements of even single molecules located in the hot-spot, unliketraditional spectroscopy methods which take an average over millions orbillions of molecules.

One goal of nanophotonics is to construct a so-called “superlens”, whichwould use metamaterials or other techniques to create images that aremore accurate than the diffraction limit (deep subwavelength).

Near-field scanning optical microscope (NSOM or SNOM) is anothernanophotonic technique that accomplishes the same goal of taking imageswith resolution far smaller than the wavelength. It involvesraster-scanning a very sharp tip or very small aperture over the surfaceto be imaged.

Near-field microscopy refers more generally to any technique using thenear-field to achieve nanoscale, subwavelength resolution. For example,dual polarization interferometry has picometer resolution in thevertical plane above the waveguide surface.

Environmental Factors

As indicated above, sensors obtain environmental factors related to,about, or near the scenes being observed. These may be called ambientfactors. The sensors may measure or sense to obtain the environmentalfactors. In other instances, the factors may be accessed, acquired,procured, etc. via another source, sensor, memory, machine, etc.

The environmental factors are abiotic or biotic. However, there areother datapoints that may be gathered, but which are not expresslyrelated to the environment. These may be called associated orobservation-related factors.

An abiotic environmental factor is associated with non-biologicalsources. That is, the source of the thing being measured is not relatedto a living or recently living thing.

Examples of abiotic environmental factor include ambient temperature,timestamp (e.g., time and date), moisture, humidity, radiation, theamount of sunlight, and pH of a water medium (e.g., soil) where abiological cell lives. Other examples of abiotic environmental factorsinclude barometric pressure; ambient sound; indoor location; ambientelectromagnetic activity; velocity; acceleration; inertia; ambientlighting conditions; WiFi fingerprint; signal fingerprints; GPSlocation; geolocation; airborne particle counter; chemical detection;gases; radiation; air quality; airborne particulate matter (e.g., dust,2.5 PPM, 10 PPM, etc.); atmospheric pressure; altitude; Geiger counter;proximity detection; magnetic sensor; rain gauge; seismometer; airflow;motion detection; ionization detection; gravity measurement;photoelectric sensor; piezo capacitive sensor; capacitance sensor; tiltsensor; angular momentum sensor; water-level (i.e., flood) detection;flame detector; smoke detector; force gauge; ambient electromagneticsources; RFID detection; barcode reading; or some combination thereof.

A biotic environmental factor is one having a biologic source. Exampleof such include the availability of food organisms and the presence ofconspecifics, competitors, predators, and parasites.

While it is not an environment factor, per se, the observation-relatedor associated factor is described here. The associated orobservation-related factor may be a measurement of quality, quantity,and/or characteristic of the environment about the observation itself orrelated to the environment from which the subject is observed or wasobtained. They may also be data that a human or computer associated withother environmental factors or the scene.

Examples of the observation-related or associated factor include nearbytraffic patterns or noises; tracking the movements of particularindividuals (e.g., via employee badges or security cameras); visitors;patients, budgets of related departments; and the like.

Herein, a known sensor or measurement device may be listed as an exampleof an environmental factor. For example, Geiger counter and seismometerare listed as examples. It should be understood that the relevant factorfor least listed examples is the measurements typically made by suchdevices. Thus, the obtained factor for example Geiger counter isradiation and the obtained factor for the example seismometer is themotion of the ground.

Artificial Intelligence, Machine Learning, and Deep Learning Technology

Herein, the term AL/ML/DL technology refers to a technology that employsknown or new artificial intelligence (AI) techniques, machine learning(ML) techniques, deep learning (DL) techniques, and/or the like.

By applying AI/ML/DL technology such as convolutional neural networks(CNNs), some implementations of the technology described herein iscapable of identifying pathobiological cells and substances withinmicroscopic images and spectrographic signatures that environment andsystems ingest from either existing datasets or streams of real-timesensor data.

By training its neural networks against libraries of high-qualitypathobiological cells/substances images and signatures, the technologydescribed herein can reliably identify specific cells/substances. Upondiscovery, the technology described herein may take advantage of asophisticated queueing system to retroactively “replay” historical datawith a greatly increased sampling rate, enabling it to build ahigh-resolution model of the outbreak. This model is then added to thechain, fully secure, attributed and available to researchers who can useit to help contain the outbreak or to advance the understanding of itstransmission model.

For example, the technology described herein can provide for real-timeand real-world data. The chip is used in the data ingest pipeline andmarketplace. Deployed throughout a building and/or across a region andusing the sensor technology to pick up environmental (e.g. temperature,humidity, etc.), visible, and spectrographic data which, coupled withambient other (e.g., location, time, etc.) data, the numerous chips inthe system can together stream enormous volumes of valuable data intothe platform for processing by the artificial intelligence insightsengine described herein. As used herein, a platform includes a remotedevice with massive storage capacity and processing power.

The technology described herein may utilize AI to detect objects thathave already been learned by a device or platform implementing thetechnology. This minimizes the amount of data transmitted, efficientlyutilizing communication bandwidth. The sensed and other data associatedwith objects that the technology described herein detects, but cannotidentify, will be sent to the platform which would, in turn, trigger aninvestigation that gathers real-world samples and take those samples toa lab for controlled analysis and identification using machine learningwith deep learning. Once identified in the lab, the platform can send aspecific detection approach to an implementation of the technologydescribed herein so that it can then confidently identify the new objectis going forward.

The technology described herein will either contain or connect tosensors that will enable novel abilities to detect pathobiological cellsand substances at low concentrations, in noisy environments (e.g.objects in the midst of dust, dirt, or other uninteresting objects), inreal-time (e.g. without significant delay from when object was in thefield of view), in some cases without disturbing the environmentdetected objects (i.e. passive observation), and with the ability tosearch large three dimensional spaces so as to reduce the probability ofnot observing an interesting object which is especially important whenthe objects are infrequently present.

Having some or all of these qualities, coupled with detection assistedby AI/ML/DL engines will facilitate detection and identification that ismuch faster than present technology, more accurate, and possible outsideof a lab environment.

Some implementations of the technology described herein utilize AL/ML/DLtechnology in the detection or identification of pathobiological cellsor substances from collected data. In other implementations, thetechnology descried herein may utilize AL/ML/DL technology in theanalysis of the collected data with metadata such as environmentalfactors collected therewith.

According to Apr. 4, 2018, cloudmayo.com article (“Difference betweenArtificial Intelligence, Machine Learning and Deep Learning”), there ismuch confusion between three different but interrelated technologies ofAI, ML, and DL. The article defines AI as a “technique that allows acomputer to mimic human behavior,” ML as a “subset of AI techniques thatuses a statistical method to enable a machine to improve withexperiences,” and DL as a “subset of ML which makes the computation ofmulti-layer neural networks feasible.”

The electronic component or collection of components that employs anAI/ML/DL technology for training and/or data analysis is called anAI/ML/DL technology herein.

System-on-a-Chip

FIG. 3 illustrates an example system-on-a-chip 400, which is animplementation of the technology described herein. As shown, thesystem-on-a-chip 400 include a semiconductor chip integrated into asingle package or as a chipset 301. Although a particular chiparchitecture is described, it is to be understood that othersemiconductor chip architectures may be implemented.

The chip 301 can be resident in different devices, such a smart phones,laptop/table computers, dedicated medical/research devices, etc. Suchdevices are used for detection of pathobiological cells or substances.In certain implementations, computing may be performed off-chip via thecloud, or performed on the chip.

The chip 301, and in particular devices including the chip 301, may useAI/ML/DL engines to process data. In particular, AI/ML/DL engines may beused by the chip 301 in the accelerated processing of collected data,such as environmental factors and other data to detect/identifypathobiological cells and substances. In addition, doing so reduces databandwidth of communication (e.g., to the platform). Also, distributedprocessing at the device reduces the cost of the device and reducescommunication bottlenecks.

The chip 301 includes a processor(s) or processing component 300, cachememory 302, security component 304, optical detection 306, and digitaldetection 308. Depending on the implementation, the digital detection308 may be used for one or more of the following: digital enhancement,object detection, or object identification.

Chip 301 can include one or more AI/ML/DL engines or accelerators 309.The AI/ML/DL accelerators 309 can implement edge and/or cloud computing.Chip 301 further can include encryption 310, and debug and tracecomponent 312.

Interfaces 314 are provided/included in chip 301. In particularinterfaces 314 provide the ability to receive sensory input, such asenvironmental factors (e.g., temperature, air pressure, wirelesssignals, capacitance, etc.) and capture image of a scene (e.g., via amicroscope, camera, or spectrometer). The interface 314 also allows fortransmission of data, network connections, user input, statusindicators, and control illumination circuitry.

Interfaces 314 may further connect, for example, to an in-packagedynamic random-access memory (DRAM) 320, in-package electricallyerasable programmable read-only memory (EEPROM) 322, and powermanagement integrated circuit (PMIC) 324.

Network of Monitoring Devices

FIG. 4 is an illustration of an infrastructure 400 that facilitates datacollection and data analysis of the technology described herein. Thedata collection, for example, may occur across a network of variouswidths (i.e., horizontally) and depths (i.e., vertically). Consider forthis example a single building. That building is called the HopeHospital 410.

The Hope Hospital 410 has many different floors, rooms, departments,etc. For illustration purpose, the Hope Hospital 410 has four floors offive rooms each. And each floor is a department.

The Hope Hospital 410 has installed a variety of electronic monitoringdevices. These devices are like smartphone 220 of example scenario 200.However, these electronic monitoring devices may be stationary andspecial-purpose. These electronic monitoring devices are called NANOBOT™devices herein. However, these NANOBOT™ devices are special-purposedevices rather than a smartphone. That is, the NANOBOT™ devices aredesigned and built for the specialized and particular purpose of thefunctionality described herein.

In this example, there are multiple NANOBOT™ devices placed throughoutthe hospital. Indeed, there could be multiple devices in each room. Forexample, a NANOBOT™ device may be installed on the bed, the bathroomcounter, hand sanitizer dispenser, the faucet handle, the air vent, andthe like. In addition, other NANOBOT™ devices may be installed in theductwork of the HVAC.

As depicted, stationary device 412 is mounted on the wall in a room ofpatient X, device 414 is on the ceiling of a room of patient Y, device416 is on the bed of patient Z, device 418 is mounted is in the ductingof the third floor, and device 420 is a portable device carried by anurse.

Each of these NANOBOT™ devices are installed to monitor biological cellsand/or substances and environmental factors in their proximity. Thedevices will detect and/or identify the particular cells and/orsubstances in their proximity. In addition, the ongoing monitoring ofthese devices enable the tracking of changes in the detected and/oridentified microscopic lifeforms, for example, in their proximity.

In addition, each of these NANOBOT™ devices include a sensor or sensorsfor monitoring one or more environmental factors, such as ambienttemperature, humidity, indoor location, and the like. Each device tracksits proximate factors and microscopic lifeforms over time.

In addition to the stationary NANOBOT™ devices, other electronic devicesmay be used in the Hope Hospital 410. For example, there may be mobileor ambulatory devices that are specially designed to do the samefunctions. These devices may be affixed to a mobile or portableplatform. Alternatively, these devices may have the ability to relocateon their own power or volition.

For example, NANOBOT™ device may be affixed to a cart, roboticmedication dispenser, a chart, and the like. As such, the NANOBOT™device tracks the environmental factors and biological cells proximatethe device as the thing to which it is affixed is moved throughout thehospital. As such, the indoor location of the device changes as well.

Similarly, a NANOBOT™ device may have its own mechanism forself-propulsion. It may have electric motors, wheels, andself-navigational capability to travel the hallways, walls, and ducts ofthe building. In addition, some self-propelled NANOBOT™ devices maytravel in the air, for example, like a so-called drone (i.e., unmannedaerial vehicle). In some instances, a self-propelled NANOBOT™ device maytravel on and/or within the liquid.

The self-propelled NANOBOT™ device may wander around the hospital orother space to generate a comprehensive amount of monitoring data forsuch space or a portion thereof. Alternatively, the self-propelledNANOBOT™ device may travel a pre-determined path or navigate its ownpath. In doing so, the device is tracking data as it travels and/or atparticular points along the path.

In addition, humans may carry a smartphone or some form of smartphoneaccessory (e.g., watch) that is capable of performing thesefunctionalities. Indeed, this type of mobile device may perform thesefunctions actively and/or passively. That is, the user may activelychoose when and where to perform measurements and/or the device maychoose when and where to perform measurements.

These various devices in the Hope Hospital 410 may be interconnectedwith each other and/or connected to a common or interconnected network430. For example, the stationary NANOBOT™ devices may be connected toeach other view a peer-to-peer mesh wireless network. Devices may beconnected via a wireless access point (WAP) or via short-range wirelesssignal (e.g., BLUETOOTH). In turn, these networks may be connected toother networks (such as the so-called Internet) and to centralized ordistributed computing center. For example, all of the stationaryNANOBOT™ devices in a room may be connected at a single nearby WAP thatis connected to the so-called cloud where the acquired data is stored ina cloud-based storage system. The network 430 represents any and all ofthese suitable communication networks.

Data Collection

Each device in the Hope Hospital 410 is configured to monitor itsproximate area. The devices monitor for biological cells and variousenvironmental factors, such as location, temperature, and humidity. Eachdevice may be configured to collect data in a manner like thatsmartphone 220 of example scenario 200.

Once the data is collected by a monitoring device, it may be locallyanalyzed or the raw data may be transferred or uploaded to a centralizedand/or distributed computing system. Supersystem 432 is depicted as aseveral servers. This represents the centralized and/or distributedcomputing system.

If analyzed locally, the collected data may be fully or partiallyanalyzed locally. With local analysis, some or all of the processingthat is necessary to detect and/or identify a class or type ofbiological cell is performed on the electronic monitoring device.Indeed, if fully processed locally, the monitoring device may form aconclusion regarding the type/class of cell in a monitored scene.

In some instances, the raw monitor data or partially processed data maybe uploaded or transferred to a computing system. For example, eachfloor of the Hope Hospital 410 may have its own dedicated computer tostore monitor data of the devices of that floor. That floor computer mayperform some or all of the calculations needed to determine thattype/class of the cells in the monitored scenes. Alternatively or inaddition, each building may have its own computer; each campus has itsown computer; each city has its own computer; each region has its owncomputer; etc. Alternatively or in addition, all of this data istransferred to the “cloud” for storage and processing overall or at eachlevel.

In addition, individuals may collect data for their own personalreasons. For example, a visitor may collect data in the cafeteria of thehospital so that she knows how clean the surfaces are. This data and/orits analysis may be uploaded to the so-called cloud. That is, the datacollection may be crowdsourced or available to the individual alone.

This data may be collected in a coordinated fashion by the Hope Hospital410 or an agency working on their behalf. The collection and analysis ofthis data may be performed by the Hope Hospital 410. In addition, thecollection and analysis of the data of Hope Hospital 410 may be aservice that the Hope Hospital 410 subscribes to.

Furthermore, a service may collect data from many and various locationsin an anonymous fashion to protect the private data of each identifiablecustomer. With the data collected from many different locations andcustomers, the service may analyze the data to find meta-trends andmeta-correlations.

The supersystem 432 includes one or both systems 440 and 460. System 440is primarily for the collection and analysis of image-based data. System460 is primarily for the inferences or conclusions of the collected andanalyzed data. The supersystem may be called the “platform” herein.

System 440 includes a data communications subsystem 442, a biologicdetection subsystem 444, and a report subsystem 448.

The data communication subsystem 442 obtains (e.g., via wirelesscommunication) image-based data from one or more of multiple remotemonitoring devices. The image-based data from each monitoring device isbased on (e.g., derived from) one or more images of a scene proximate tothat monitoring device. The proximate scene includes biological cellsand/or substances therein.

The data communication subsystem 442 also obtains (e.g., via wirelesscommunication) environmental data from one or more of the multipleremote monitoring devices. The environmental data being based on anenvironmental factor associated with the in-scene biological cellsand/or substances of each device or the environment surrounding thein-scene biological cells and/or substances of each device.

The biologic detection subsystem 444 analyzes the image-based dataand/or environmental data. Based on that analysis, the biologicdetection subsystem 444 detects and/or identifies a type or class ofbiological cells and/or substance amongst the in-scene biological cellsand/or substances of each device or amongst several of the devices.

To accomplish detection, the biologic detection subsystem 444 may employon and/or employ a database 446. This database 446 may be a database ofbiologic-cellular or biologic-substantive signatures. This may be calleda training corpus. A training corpus is a database of numerousapplication-specific samples from which the AI/ML/DL engine “learns” andimproves its capabilities and accuracy.

The biologic detection subsystem 444 employs an AI/ML/DL engine toperform or assist in the performance of the detection and/oridentification of one or more biological cells and/or substances.

The AI/ML/DL engine functionality may be split across the platform. Thatis, the devices may perform pre-processing of the image using AI/ML/DLengine and send the results as the image-based data to the system 440for further processing herein. In that case, the device 414 communicatesin real time (or nearly so) with the platform. In this way, theintensive processing is offloaded from the devices to the platform.

The biologic detection subsystem 444 analyzes the image-based data ofthe scene to detect, determine, and/or identify the type or class ofbiological cells and/or substances therein. It may do this, at least inpart, by using distinguishing molecules of the cells that are observableusing the electromagnetic spectrum. In some implementations, other data(such as chemical reactions or excitation) may be included in theanalysis.

The report subsystem 448 reports the detection and/or identification ofthe type of biological cells and/or substances in the scene proximate tothe monitoring device. The report subsystem 448 may send 449 its resultsand associated data (image-based data and/or environmental data) to thesystem 460.

Data Analysis

As discussed herein, an electronic device captures a scene that hasbiological cells therein. In some instances, these biological cells maybe described as in situ (i.e., in place) because they are monitored,examined, tested, etc. where they naturally live, inhabit, or exist.That is, the biological cells have not been moved, relocated, orexpatriated in order to perform the examination, testing, or the like.

Using a camera or digital or other imaging technology, the electronicdevice captures a portion of the electromagnetic spectrum that isemitting or reflecting from the matter contained in the scene. Theobtained image is micrographic, spectrographic, digital, or somecombination thereof. The obtained image is micrographic because itcaptures elements in the scene that are on a microscopic scale. Theobtained image is spectrographic because it captures elements in thescene by using equipment sensitive to portions of the electromagneticspectrum (visible and/or non-visible portions). The obtained image isdigital because it formats and stores the captured information as datacapable of being stored in a machine, computer, digital electronicdevice, or the like.

While the thing that is captured is called an image, this image is notnecessarily displayable as a two-dimensional depiction on a displayscreen. Rather, the image is an array of data that represents thequantitative and qualitative nature of the electromagnetic spectrum (orsome portion thereof) received by the camera of the electronic devicewhen it was exposed to the scene.

On its own or working with other devices or computer systems, theelectronic device analyzes the image of the scene to detect, determine,and/or identify the type or class of biological cells therein. It may dothis, at least in part, by using distinguishing molecules of the cellsthat are observable using the electromagnetic spectrum. That is, theelectronic device captures the observable electromagnetic spectrum(e.g., visible and/or non-visible) that is reflected, scattered,emitting, etc. from the in-situ cells of a captured scene to determinethe molecules of those cells.

Some of these molecules are indicative of certain classes, types, orparticular cells. Such molecules are called marker biomolecules herein.The electronic device can determine which cell types or class arepresent in a captured scene by the on the particular ones of, the typesof, and the proportions of the biomolecules detected therein.

In addition, the electronic device may includes or may have one or moreenvironmental sensors that are designed to measure one or moreenvironmental factors associated with the in situ biological cells orthe environment surrounding the in situ biological cells.

The electronic device may have or connect to a report system that isdesigned to report a detection and/or identification of the one or morecell types or classes in the obtained image and in some implementations,the report system is designed to associate the measured environmentalfactor(s) with the obtained image and/or with the detected cell.

Correlation Engine

The system 460 includes a correlation engine 462. Based on theimage-based data from multiple devices, environmental data from multipledevices, any other associated data, and the results from the reportsubsystem, the correlation engine 462 finds hidden patterns andultimately discovers underlying causes of activity (or lack thereof) ofbiological cells and/or substances. The correlation engine 462 includesone or more AI/ML/DL engines.

Two of the categories of data are supplied to the correlation engineinclude cellular/molecular observations and environmental observations.One of the categories is based on the cellular and/or molecularmeasurements/observations of the scene itself. It may be the fullconclusion about the detection/identification of the type/class of cellsand/or substances found in the scene or something less than the fullconclusion.

The other category is any other environmental factor. Of course, thereis a myriad of choices and the mass amount of data available here.Because of this, the tools of the so-called Big Data are employed toaddress this.

Inference Engine

The system 460 includes an inference engine 464, which may beimplemented with AI/ML/DL engines. Based on the image-based data frommultiple devices, environmental data from multiple devices, any otherassociated data, the results from the report subsystem, and thecorrelations of the correlation engine 462, the inference engine 464 candraw inferences based on the patterns or links detected by thecorrelation engine. For example, there may be a direct correlationbetween ambient humidity and the proliferation of a specific type ofbiological cell. The inference is that humidity directly affects thattype of cell's growth.

If the cell is deemed to be bad, then a human may decide to implement asolution to control the humidity more closely to control the growth moreclosely. Alternatively, with sufficient automation in place, acomputer-controlled HVAC system may be directed to lower the humidity ofa room to lessen the chances of the growth of that type of cell.

If strong inferences are formed from the analysis of the data, then moreaccurate predictions can be made. For example, with sufficientinformation gathered over a large enough area in real time, an epidemicof an infectious disease may be detected and its spread predicted soearly the spread of the epidemic may be halted long-before the epidemiccould take hold.

To further the goal of making better inferences and predictions, thetools of the so-called Big Data may be employed. Big Data is an evolvingterm that describes the tools used to work with a voluminous amount ofstructured, semi-structured and unstructured data that has the potentialto be mined for information.

Big Data works on the principle that the more you know about anything orany situation, the more reliably you can gain new insights and makepredictions about what will happen in the future. By comparing more datapoints, relationships will begin to emerge that were previously hidden,and these relationships will enable us to learn and inform ourdecisions.

Most commonly this is done through a process which involves buildingmodels, based on the data we can collect, and then running simulations,tweaking the value of data points each time and monitoring how itimpacts our results. This process is automated—today's advancedanalytics technology will run millions of these simulations, tweakingall the possible variables until it finds a pattern—or an insight—thathelps solve the problem it is working on.

Increasingly, data is coming to us in an unstructured form, meaning itcannot be easily put into structured tables with rows and columns. Muchof this data is in the form of pictures and videos—from satellite imagesto photographs uploaded to social networking sites—as well as email andinstant messenger communications and recorded telephone calls. To makesense of all of this, Big Data projects often use cutting edge analyticsinvolving artificial intelligence and machine learning. By teachingcomputers to identify what this data represents—through imagerecognition or natural language processing, for example—they can learnto spot patterns much more quickly and reliably than humans.

A strong trend over the last few years has been a move towards thedelivery of Big Data tools and technology through an “as-a-service”platform. Businesses and organizations rent server space, softwaresystems and processing power from third-party cloud service providers.All of the work is carried out on the service provider's systems, andthe customer simply pays for whatever was used. This model is making BigData-driven discovery and transformation accessible to any organizationand cuts out the need to spend vast sums on hardware, software, premisesand technical staff.

Distributed Ledgers

With the platform, users may upload and share their own data related tobiological tests, experiments, etc. Their data becomes part of the BigData collection of data and may help form better inferences for others.

As part of this service, the data may be stored in a blockchain fashionto ensure that the data is not altered, deleted, or manipulated. Ablockchain is a digitized, decentralized, distributed, public ledger ofdata transactions. Constantly growing as ‘completed’ blocks (the mostrecent transactions) are recorded and added to it in chronologicalorder, it allows participants to keep track of data transactions withoutcentral recordkeeping. Each node (a computer connected to the network)gets a copy of the blockchain, which is downloaded automatically.

Example Processes

FIG. 5 is a flow diagram illustrating example process 500 that implementthe techniques described herein for the data collection and/or analysisfor the detection and/or identification of biological cells and/orbiological substances. For example, the example process 500 may detectand/or identify pathobiological cells and/or substances.

The example process 500 may be performed, at least in part, by theelectronic device 230, by the system-on-a-chip 301, and/or system 440 asdescribed herein. The example process 500 may be implemented by otherelectronic devices, computers, computer systems, networks, and the like.For illustration purposes, the example process 500 is described hereinas being performed by a “system.”

At block 510, the system obtains (e.g., via wireless communication)image-based data from one or more of multiple remote monitoring devices.The image-based data from each monitoring device is based on (e.g.,derived from) one or more images of a scene proximate to that monitoringdevice. The proximate scene includes biological cells and/or substancestherein. Indeed, the system may obtain image-based data based on asequence of images of the scene. If any particular type or class ofbiological cells and/or substances are detected, then the scene includedbiological cells and/or substances.

A scene may include, for example, one or more surfaces on which thein-scene biological cells and/or substances inhabit; a liquid in whichthe in-scene biological cells and/or substances inhabit; a bodily fluidin which the in-scene biological cells and/or substances inhabit; anarea in which the in-scene biological cells and/or substances inhabit; avolume in which the in-scene biological cells and/or substances inhabit;an area or volume with its dimensions falling below 0.1 mm; or acombination thereof.

In some implementations, the scene includes biologic cells or biologicsubstances. But, often, the scene includes both. The in-scene biologicalcells and/or substances may be characterized as: physically located on asurface; physically located in a medium (e.g., blood, bodily fluids,water, air, etc.); undisturbed in their environment; undisturbed andunadulterated; physically located on a surface in a manner that isundisturbed and unadulterated; not relocated for the purpose of imagecapture; unmanipulated for the purpose of image capture; or on a surfacethat is unaffected by the scene-capture system.

In some implementations, the biologic cells and/or substances are insitu and in other implementations, they are in the lab.

The obtained image is micrographic, spectrographic, and/or digital. Insome implementations, the obtained image is micrographic because theimage of the scene is captured at least in part: on a microscopic scale;using microscope like magnification; includes microscopic structures andfeatures; includes structures and features that are not visible to anaked human eye; or a combination thereof.

In some implementations, the obtained image is spectrographic at leastin part because the image of the scene is captured using some portion ofthe electromagnetic spectrum (e.g., visible spectrum of light, infrared,x-rays, gamma rays, ultraviolet) as it interacts with matter, suchinteractions include, for example, absorption, emission, scattering,reflection, and refraction.

The image may be obtained by capturing a digital image of the scene, andthat scene may include in-scene biological cells and/or substancestherein. In addition, digital enhancement of a captured digital imagemay be employed to better reveal the in-scene biological cells and/orsubstances in the captured image. The obtained image is digital at leastin part because the image of the scene has handled as a set ofmachine-readable data.

At block 520, the system analyzes the image-based data and/orenvironmental data. Based on that analysis, the system detects and/oridentifies a type or class of biological cells and/or substance amongstthe in-scene biological cells and/or substances of each device oramongst several of the devices.

The system may employ an AI/ML/DL engine to perform or assist in theperformance of the detection and/or identification of one or morebiological cells and/or substances.

The AI/ML/DL engine functionality may be split across the platform. Thatis, the devices may perform pre-processing of the image using AI/ML/DLengine and send the results as the image-based data to the system 440for further processing herein. In that case, the device 414 communicatesin real time (or nearly so) with the platform. In this way, theintensive processing is offloaded from the devices to the platform.

The biologic detection subsystem 444 analyzes the image-based data ofthe scene to detect, determine, and/or identify the type or class ofbiological cells and/or substances therein. It may do this, at least inpart, by using distinguishing molecules of the cells that are observableusing the electromagnetic spectrum. In some implementations, other data(such as chemical reactions or excitation) may be included in theanalysis.

Examples of one or more of the types or classes of biological cells thatmay be detected and/or identified at block 520 includes (by way ofexample, but not limitation): cells of a multicell biological organism;cells of a tissue or organ of a multicell biological organism; cells ofa tumor or growth multicell biological organism; single-celled organism;microbes; microscopic organisms; single-celled organism; living thingsthat are too small to be seen with a human's naked eye; a biologicalcreature that can only be seen by a human with mechanical magnification;microscopic spores; a combination thereof. In addition, the biologicalcells have a size range that is selected from a group consisting of:10-100 nanometers (nm); 10-80 nm; 10-18 nm; 15-25 nm; and 50-150 nm.

Furthermore, biological cells and/or substances may be typed orclassified as pathobiological, not pathobiological, pathobiologyunknown, or pathobiology not-yet-known. The pathobiological biologicalcells and/or substances may be classified or typed as (by way of exampleand not limitation): pathobiological cells; pathobiological substances;toxic; poisonous; carcinogenic; diseased cells; cancer cells; infectiousagents; pathogens; bioagents; disease-producing agents; or combinationthereof.

Of those biological cells that are characterized as microbes, they maybe further typed or classified as one or more of the following (by wayof example and not limitation): single-celled organisms; bacteria;archaea; fungi; mold; protists; viruses; microscopic multi-celledorganisms; algae; bioagents; spores; germs; prions; a combinationthereof.

In some instances, the operation at block 520 may include anidentification of one or more particular biologic cells and/orsubstances in the scene. Rather than just detecting the type or class(e.g., pathogen), the operation may identify the member of that type orclass (listeria). Examples of particular members of a class or type thatthis operation may identify include: Clostridium botulinum,Streptococcus pneumoniae, Mycobacterium tuberculosis, Escherichia colio157:h7, Staphylococcus aureus, Vibrio cholerae, ebola, hiv, influenzavirus, norovirus, zika virus, aspergillus spp, and entamoebahistolytica.

At block 520, one or more implementations of the detection and/oridentification includes operations to:

-   -   access a database of signatures of biological cells and/or        substances;    -   isolate a biological cell and/or substance in the obtained        image;    -   correlate the isolated biological cell and/or substance to at        least one signature in the database;    -   determine that the correlation is significant enough to indicate        a sufficient degree of confidence to identify the isolated        biological cell and/or substance as being a biological cell        and/or substance;    -   in response to that correlation determination, label the        isolated biological cell and/or substance as being the        determined biological cell and/or substance.

An example of a “sufficient degree of confidence” includes more likelythan not. A confidence factor for the “sufficient degree of confidence”may be weighted relative to a perceived degree of threat. For example, apathogen that is unlikely to cause a human infection may have a veryhigh confidence factor (e.g., 80%). Thus, a detection may only be notedif it is at least 80% likely to be that particular pathogen. Conversely,a pathogen may be particularly dangerous (e.g., small pox) and have onlya small confidence factor (e.g., 20%). In this way, the dangerouspathogen is detected even it is more likely that the pathogen wasmisdetected.

Other implementations of the detection and/or identification includeoperations to:

provide the obtained image to a trained biologicaldetection/identification (detection and/or identification) engine, thetrained biological detection/identification engine being an AI/ML/DLengine trained to detect and/or identify biological cells and/orsubstances based on a training corpus of signatures of biological cellsand/or substances;

receive a positive indication from the biologicaldetection/identification engine that the obtained image includes abiological cell and/or substance therein and/or the identity of thatbiological cell and/or substance.

Still other implementations of the detection and/or identificationinclude operations to:

-   -   provide the obtained image to a trained biological        detection/identification engine, the trained biological        detection/identification engine being an AI/ML/DL engine trained        to detect and/or identify pathobiological cells and/or        substances based on a training corpus of signatures of        pathobiological cells and/or substances;    -   receive a positive indication from the biological        detection/identification engine that the obtained image includes        a pathobiological cell and/or substance therein and/or the        identity of that pathobiological cell and/or substance,    -   wherein the obtained image includes data captured from the        visible and/or non-visible electromagnetic spectrum of the        scene.

Other variations of the detection and/or identification operationsdescribed above may be focused on pathobiological cells and/orsubstances in particular.

At block 530, from each of the multiple devices, the system gathers oneor more environmental factors associated with the in-scene biologicalcells and/or substances or the environment surrounding the in-scenebiological cells and/or substances. The environmental data being basedon an environmental factor associated with the in-scene biological cellsand/or substances of each device or the environment surrounding thein-scene biological cells and/or substances of each device. In someimplementations, the system may acquire information related to orassociated with the scene.

The measured environmental factors include (but are not limited to):

temperature; timestamp (e.g., time and date, local time, incrementaltime, etc.), humidity; barometric pressure; ambient sound; location;ambient electromagnetic activity; ambient lighting conditions; WiFifingerprint; signal fingerprints; GPS location; airborne particle orchemical detector/counter; gases; radiation; air quality; atmosphericpressure; altitude; Geiger counter; proximity detection; magneticsensor; rain gauge; seismometer; airflow; motion detection; ionizationdetection; gravity measurement; photoelectric sensor; piezo capacitivesensor; capacitance sensor; tilt sensor; angular momentum sensor;water-level (i.e., flood) detection; flame detector; smoke detector;force gauge; ambient electromagnetic sources; RFID detection; barcodereading; or a combination thereof.

At block 540, the device reports a detection and/or identification ofthe type of biological cell and/or substances in the scene proximate tothe monitoring device. For example, the device may provide a report ornotification via a user interface to a user. A messaging system (e.g.,email or SMS) may be used for such notification.

In some implementations, the system may report that the type ofbiological cell and/or substances in the obtained image is a categoryflagged for further research and inquiry. For example, the device may beunable to detect the type of cell or substance. In that case, the deviceflags this as a something worthy of further inquiry. This category maybe the default when there is a failure to detect or identify a cell orsubstance. In some instances, this category is only triggered withparticular markers (e.g., chemicals or structures) are detected.

The report or notification may include the following (by way of exampleand not limitation) operations: send a communication (e.g., message,postal mail, email, text message, SMS message, electronic message, etc.)to a human or machine that is designated to receive such communicationsvia wired or wireless communications mechanisms; send a notification(e.g., message, postal mail, email, text message, SMS message,electronic message, push notices, etc.) to a human or machine that isdesignated to receive such notification via wired or wirelesscommunications mechanisms; update a database designated to receive suchupdates via wired or wireless communications mechanisms; store in memory(e.g., local or remote) the detection; or a combination thereof.

In addition, at block 540, the device associates the measuredenvironmental factor and/or the associated factor with the obtainedimage and/or with the detected type or class of biological cell and/orsubstance. This association may perform in one more database. Indeed,such a database may take the form of a distributed ledger (DL)technology.

As part of block 550, the report operation may send 449 its results andassociated data (image-based data and/or environmental data) to thesystem 460.

At block 560, the system 460 performs correlation, inference, ranking,and/or amelioration operations in response to the results and associateddata (image-based data and/or environmental data) sent 449 to system460.

Distribution of Scientific Data

The environment and systems can be made available and remove borders toattract the largest network of professional and citizen scientists fromaround the world to solve the grand challenges of human andenvironmental health through a sincere, transparent and purpose-drivensystem.

Using the blockchain and the Coin, an incentive and reward-basedeconomic system can be established to enable the formation of a largeaggregated molecular dataset.

Artificial Intelligence, machine learning, and deep learning forhealthcare may be used to accelerate development on many fronts.Throughout the industry, biologists and computer scientists are workingtogether to design experiments that incorporate artificial intelligenceto identify elements of data that might otherwise be overlooked topredict and treat disease, classify disease types, understand diseasesub-populations, find new treatments and match them with the appropriatepatients. This is progress, but the model is limiting because itrequires us to have access to the right datasets first.

Even with relatively limited aggregated datasets, the application ofartificial intelligence, machine learning, and deep learning bycomputational biologists, researchers, scientists and medicalprofessionals can provide positive outcomes. It is the intent of havethe environment and systems described herein to increase the scale ofcollected and created data by orders of magnitude more and applied it toall global health threats.

The environment and system can accelerate the rate of innovation inhuman and environmental health by incentivizing the aggregation ofglobal datasets and creating new real-time datasets, enablingscientists, researchers and artificial intelligence systems to make newdiscoveries on an unprecedented scale.

By converging key health care technologies, a global system can providea decentralized economy that incentivizes data sharing on a global scaleusing blockchain and the cryptocurrency or Coin, to enhance the speedand efficiency of medical discovery. The Coin or coins will be minted inreal time to convert global data to tangible value.

Although scientists and researchers are taking advantage of all thetools available to them by working hand-in-hand with computer scientiststo design experiments that harness the power of artificial intelligence,machine learning and deep learning, but these tools are limited becausethey require access to the right datasets first. By incentivizinginstitutions, organizations, governments or individuals globally to pooldatasets into one shared ecosystem, we will create an enormous,multi-dimensional, global dataset.

By collecting both research-based and real-world molecular data fromboth partner institutions and marketplace contributors, all of whom willbe compensated for their contributions with the Coin, creating adecentralized economy can be created in pursuit of better health. Theblockchain technology means that each piece of data collected isattributed to its correct source, so if a scientist contributes datathat is ultimately used by an institution to develop a critical healthsolution, that scientist will get credit and compensation for theirimportant contribution to the cause, just like a musician would receiveroyalty compensation for the use of his music.

All of the data contributed to the environment will filter through anartificial intelligence, machine learning and deep learning system,which will be used by marketplace participants to identify patterns,find new correlations and draw conclusions at a scale never beforereached. These insights will be added back into the shared database foranyone to access. We firmly believe that by providing the world'sbrightest minds with this tool, we can dramatically accelerate the speedand efficiency of cure development.

The more data that is collected, the faster and more effectivediscoveries can be made. Additionally, a particular chip architecture isprovided. The chip will have the ability to collect data in real time,either from real-world environments or laboratories, meaning that alldata ranging from a spectroscopy machine in a lab to tabletop germs in ahousehold can be added to the environment in real time.

For example, in a decentralized economy an initial task may be tocollect data and build a dataset. Just as the Human Genome Projectdecreased the cost of sequencing a human genome by roughly one-millionfold between 1990-2003, the use of described technologies can decreasethe cost and time associated with scientific discovery.

The environment can be used as a tool by professional scientists andinstitutions around the world to remove borders and encouragecollaboration and efficiency around major global health challenges. Inaddition to these key players in today's science ecosystem, there isanother person we hope to empower: the citizen scientist.

It is expected that the environment is open for participation to peoplearound the world, regardless of institutional affiliation orgeographical location. A major institution might tap into the platform'sdatabase of lab-based and real-world superbug data to develop a criticalsolution to antimicrobial resistance, or an individual might contributeimportant molecular data about the conditions and materials that promoteblack mold growth by placing a chip (device using chip) in their home.Citizen scientists can also participate in the economy simply byproviding funding, or purchasing the Coin(s), to target particulardisease areas. For example, someone with a family history of Alzheimer'sdisease might want to purchase Coins to direct funding toward furtherresearch to have a tangible part in the mission to find a treatment. Thegoal is to unleash both the professional scientist from his or herinstitutional boundaries and encourage the citizen scientist toparticipate directly in the process of developing cures.

In particular scenarios, the environment exists to accelerate thedevelopment of cures for the global health threats that impact all ofus. An initial list of key disease areas to target may be created, withnew disease areas added in time.

Anyone in the world can contribute to the development of cures (whichcould include preventive medicine, therapeutics, vaccines or new drugdevelopment) for any of these disease areas by simply purchasing Coinsand choosing which area to fund. As we continue to add more categoriesto the list, anyone will be able to have a direct impact on thedevelopment of cures for the diseases that matter most to them.Eventually, all global illnesses may be represented.

An example of initial disease targets, which collectively affectbillions of people globally, can include: Oncology, Pancreatic cancer,Brain cancer, Virology, Hepatitis C, Influenza, Respiratory syncytialvirus, Neglected tropical disease, Bacteriology, Methicillin-resistantStaphylococcus aureus (MRSA), Clostridium difficile (C. Diff),Neurology, Alzheimer's Disease, etc.

Each of these illnesses needs new treatments and effective preventivemeasures. In today's world, the development of a new treatment takes 10to 15 years, and even then, 90% of drugs tested in clinical trials fail,effectively resetting the clock.

By mobilizing “citizen scientists” (all parties, including researchersand scientists) contribute to the environment and its economy, theprogress of finding solutions and cures can by shortening that timeframeto a fraction of the time and reducing failure rates to ensure thatthose suffering from these illnesses are considered and cared for.

The environment, system (platform) may use an inference engine,artificial intelligence, machine learning and deep learning. By forminga large global dataset, this continuous and massive collection ofmolecular data will enable artificial intelligence, machine learning,and deep learning systems to act as an insight engines by identifyingtrends and drawing conclusions, giving scientists and researchers anentirely new tool to create life-saving solutions. Use of particularchip architectures, designs, open source libraries, data sets, machinelearning, and other technologies allows for an artificial intelligencesystem's capabilities to derive unique perspectives and insights fromthis novel, global dataset.

In certain implementations, blockchain is used; blockchain withartificial intelligence; blockchain with artificial intelligence and thechip; and blockchain with artificial intelligence, the chip, andartificial intelligence, machine learning, deep learning. Using thechip, the artificial intelligence system will have access to a verylarge scale of data that will enable the system's correlations andpatterns to drive scientific research priorities and cure development.

The datasets attached to environment and systems have innate value, butalso have additional value derived from applied machine learning. Theenvironment enhances the utility of the datasets by attaching contextualmetadata, helping the learning routines understand and correlate them.The platform mines for insights within the marketplace by aggressivelyinterrogating the ingest pipeline. Incoming data is passed toindividualized handlers which feed them to a series of machine learningalgorithms custom-built for the dataset that is processed, exposing newand perhaps unexpected insights. These insights are minted into new,carefully attributed assets on the chain, increasing the marketplace'soverall value and offering researchers new avenues for acceleratingtheir work.

By applying deep learning tools such as convolutional neural networks(CNNs) the platform is capable of identifying pathogens withinmicroscopic images and spectrographic signatures that environment andsystems ingest from either existing datasets or streams of real timesensor data. By training its neural networks against libraries ofhigh-quality bacteriological images and signatures, the platform canreliably identify specific pathogens. Upon discovery, the platform takesadvantage of a sophisticated queueing system to retroactively “replay”historical data with a greatly increased sampling rate, enabling it tobuild a high-resolution model of the outbreak. This model is then addedto the chain, fully secure, attributed and available to researchers whocan use it to help contain the outbreak or to advance the understandingof its transmission model.

The chip (chip implemented device) can provide for real-time andreal-world data. The chip is used in the data ingest pipeline andmarketplace. Deployed throughout a building and/or across a region andusing the sensor technology to pick up environmental (e.g. temperature,humidity, etc.), visible, and spectrographic data which, coupled withother (e.g. location, time, etc.) data, the numerous chips in the systemcan together stream enormous volumes of valuable data into the platformfor processing by the artificial intelligence insights engine describedabove.

The chip will utilize artificial intelligence to detect objects thathave already been learned by the platform, minimizing the amount datatransmitted to the platform, efficiently utilizing communicationbandwidth. The sensed and other data associated with objects that thechip detects, but can't identify, will be sent to the platform whichwould in will turn trigger an investigation that gathers real-worldsamples and take those samples to a lab for controlled analysis andidentification using machine learning with deep learning. Onceidentified in the lab, the platform can send a specific detectionalgorithm to the chip so that it can then confidently identify the newobject going forward.

The chip will either contain or connect to sensors that will enablenovel abilities to detect biological objects at low concentrations, innoisy environments (e.g. objects in the midst of dust, dirt, or otheruninteresting objects), in real-time (e.g. without significant delayfrom when object was in field of view), in some cases without disturbingthe environment detected objects (i.e. passive observation), and withthe ability to search large 3 dimensional spaces so as to reduce theprobability of not observing an interesting object which is especiallyimportant when the objects are infrequently present. Having some or allof these qualities, coupled with artificial detection algorithms willfacilitate detection and identification that is much faster than presenttechnology (typically a sample is collected and cultivated/grown in alab environment over days), more accurate, and possible outside of a labenvironment. Sensors that may be employed to this end are snapshothyperspectral imaging (to capture a frequency spectrum over many pointsin a two-dimensional area) and/or light-field/plenoptic imaging (tofacilitate the capture, both with w/fast frame rate. A novel combinationof both these technologies may be used.

The chip has the ability to interact with the platform, allowing remoteconfiguration of chip-based devices. Some examples of configurationcapabilities are a) updating the imbedded code for the chip, b)adjusting sensor configurations and interfaces, 3) downloading improvedor new detection algorithms, and d) changing the detection polling dutycycle. In order to facilitate heightened awareness during criticalperiods, the platform might shorten the polling cycle of devices inaffected regions. To optimize power management, the platform mightlengthen polling cycles or turn off less pertinent sensors. In order todiminish scalability concerns, the chip will have processingcapabilities that will scale the frequency and detail level of outboundcommunications to the platform.

Vitally, the chip leverages its sophisticated encryption and anti-spyingsecurity capabilities to ensure the ownership and value of the dataand/or its privacy are secure. The platform, at the chip and/orelsewhere in the system will further protect the data and provide amarketplace for it using blockchain technology.

The environment and systems make use of distributed ledger of theblockchain. The distributed ledger provides the ability to mint thetokens or coins that the marketplace is built upon, as well as thesecurity needed to safely store the transactions that accompany thebuying and selling of healthcare and medical data. The marketplace canbe both centralized and decentralized, granting participants controlover their own transactions.

In addition to features that environment and systems derive from theblockchain, smart contracts may be implemented as well to ensure secureand immutable attribution. This is a feature that ensures that theauthor of a dataset is eternally associated with it, and thatrelationship will persist even if it is bought and used by multiplegenerations of consumers. This mechanism is part of the incentive systemwhich drives liquidity within the marketplace.

The blockchain can make use of and leverage other blockchain platforms,consensus models, and design models that provide or enable functionalitythat a) accommodates the needs of its marketplace for certification andattribution of data contributions and transactions, b) support verylarge numbers of IoT devices powered by its Nano Sense chipscontributing a continual real-time stream of molecular data,environmental, and other data, and c) has the ability to enable users tosecurely access and leverage a big data system.

The following describes research-based data. Traditional molecular dataincludes high throughput molecular data such as, Omics: Genomics,Lipidomics, Proteomics, Glycomics, Metabolomics, TranscriptomicsMolecular composition: spectroscopy, UV/Vis Circular Dichroism Molecularstructure: Mass spectroscopy, Hydrodynamic methods, X-raycrystallography, Nuclear magnetic resonance, Light scattering, DumasMethod, Enzyme-linked immunosorbent assay (ELISA), ImmunohistochemistryMolecular Function: Protein affinity chromatography,Co-immunoprecipitation, Fusion of protein with a molecular tag, such asgreen fluorescent protein (GFP), DNA footprinting, Gene expression.Cellular Metabolism includes: Microplate based assays, ATP assays,Mitochondrial assays, Reactive oxygen species (ROS), oxidative stress,and metabolism assays Cellular Phenotype: Microscopy, Flow cytometry,Western Blot, Genetic screen Measure of Cellular Metabolism.

The following are resulting correlations derived from systems biology.Through the use of experimentally derived data, computational biologyand biostatistics, systems biology develops systems, frameworks andmodels for integrative analysis of omics data, imaging and clinical datato identifying key molecules driving pathophysiology.

Real-world data can include epidemiological data, clinical data such ashealthcare databases and clinical data outcomes. Real-world data canalso include patient data from patient registries, pharmacy and heathinsurances databases; social media from patient-powered researchnetworks. Real-world data can also include real-world molecular datafrom: Chemical Data, Spectroscopy, Imaging, Microscopy, Environmental,Pressure, Humidity, Light, Gas, Temperature, and pH.

The following discusses impact of infectious disease on chronic disease.It is know that various infectious diseases determinately lead tonon-communicable chronic diseases such as cancer, immune-mediatedsyndromes or neurodevelopmental disorders. Advances in epidemiologicalreporting and research-based molecular technologies have enabled thesecorrelations to be made, but require assumptions due to the size of thedata sets used. Recognizing the relationship between infectious andchronic disease states can lead to ground-breaking health advancesworldwide.

The environment and systems, and particularly the marketplace enablesessential aggregation, scaling and correlation of critical moleculardatasets such as genomics, proteomics, transcriptomics and metabolomicsand real-world datasets including clinical, epidemiological, andreal-world molecular data sets specific to various pathogens chronicdisease states. Such a marketplace would enable the merging of currentlysiloed or individual data pools toward the common effort of chronic andinfectious disease prevention, diagnosis and treatment.

Biological systems are complex. When an issue arises, multiple variablesshould be considered to identify the most effective solutions. Whilecurrent therapeutic and diagnostic technologies are improving over time,their development is only incremental due to this single target, passiveapproach. Antimicrobial resistance (AMR) is a severe issue in human andenvironmental health. When drugs and preventative measures are designedto target specific proteins critical to the health and metabolism of theharmful bacterial cell, the cell can mutate overtime and resist thedesired effect. There are multiple mechanisms of AMR, involving avariety of targets with in the cell.

The marketplace takes on these complicated issues more holistically,through the value of aggregated lab-based and real-world data at scale,factoring in and connecting all relevant and known markers in a givensystem. This allows for data to be aggregated, scaled and correlated toenable all constitutes of each critical pathway to be monitored,evaluated and targeted, leading to more robust and effective solutionsto this detrimental issue.

Described blocks/components are resident in a distributed network, suchas the Internet. The blocks or components, unless otherwise particularlydescribed, are functionally described. In other words, a particularblock/component may not necessarily be resident at one or more hardwaredevices, and may operate over several devices in the network.

Now in reference to FIG. 6, an example platform architecture 600 isshown. An application program interface or API 602 connects chips 604 toa blockchain 606. Data providers 608 through API 602 are also connectedto the blockchain 606. A marketplace 610 is connected to the blockchain606. Chips 604, data providers 608 and marketplace 610 provide data tothe blockchain 606. The marketplace 610 provides data to coin holders612 through API 602.

The blockchain 606 provides data to an ingest pipeline 614, whichfurther provides data to inference engines 616. Inference enginesprovide data to analytics 618. The analytics 618 provides data to exportmanager 620. Through the API 602, export manager provides data to dataconsumers 622. The platform architecture 600 can further include rawstorage 624.

Now in reference to FIG. 7, an example system 700 is shown. ADecentralized Authority Organization or DAO 702 is a decentralizedauthority. Global Citizen Scientists, or any party, are allowed to votethrough the DAO 702. Example of voting include determine what percentageof crypto currency (i.e., coin) that they own/control is to be used. Forexample, percentage of crypto currency used for medical research,non-profit organizations, charities, etc. Various voting algorithms maybe implemented by the DAO 702. DAO 702 is connected to a Smart Contractsbucket/database or Smart Contracts 704.

DAO 702 is further connected to blockchain 706. Blockchain 706 providescrypto currency, or tradeable tokens, or as described above, the coin.Example of tradeable tokens include ERC20 defined tokens as provided bythe Ethereum network protocol. Blockchain 706 communicates with aCentral Data Pool 708. The Central Data Pool 708 also communicates withSmart Contracts 704.

Blockchain 706 communicates with both the DAO 702 and a CentralizedAuthority Manager 710. Blockchain 706 may be managed by the DAO 702 andthe Centralized Authority Manager 710. The Centralized Authority Manager710 further communicates with Smart Contracts 704, and can create andmanage contracts in Smart Contracts 704. Smart Contracts 704 can includehyper ledgers.

The Blockchain 706 communicates with Artificial Intelligence 712. Incertain implementations, Artificial Intelligence 712 may be cloud-basedand provide/manage data storage.

The system 700 further includes a chip 714. The Chip 714 can be residentin different devices, such a smart phones, laptop/table computers,dedicated medical/research devices, etc. Such devices (i.e., chip) areused for detecting biodata. The devices can also be used for mintingcoins. The Chip 714 communicates with Blockchain functional block, whichwill be located within the Chip 714 or elsewhere in the Platform, tomint the data detected by it. In certain implementations, computing maybe performed off-chip via the cloud, or performed as edge computing.

The Chip 714, and in particular devices implementing the Chip 714, willuse artificial intelligence and all or some of the Chip versions may useartificial intelligence machine learning w/deep-learning (AI/ML/DL),performed by the block Artificial Intelligence, Machine Learning, DeepLearning 716, to process data. In particular, AI/ML/DL 716 may be usedby the Chip 714 in the accelerated processing of bio-data (AI to detectand identification the bio-objects, AI ML w/DL to train and thusoptimize AI detection and identification algorithms in the controlledenvironment of a lab), bio currency, etc. AI/ML/DL 716 may becloud-based.

FIG. 8 illustrates a system 800 configured that facilitates distributionof scientific data, in accordance with one or more implementations. Insome implementations, system 800 may include one or more servers 802.Server(s) 802 may be configured to communicate with one or more clientcomputing platforms 804 according to a client/server architecture and/orother architectures. Client computing platform(s) 804 may be configuredto communicate with other client computing platforms via server(s) 802and/or according to a peer-to-peer architecture and/or otherarchitectures. Users may access system 800 via client computingplatform(s) 804.

Server(s) 802 may be configured by machine-readable instructions 806.Machine-readable instructions 806 may include one or more instructionmodules. The instruction modules may include computer program modules.The instruction modules may include one or more of data obtaining module808, sci-data analysis module 810, sci-data offering module 812,sci-data delivery module 814, sci-data associating module 816, valuedetermination module 818, value minting module 820, cryptocurrencyawarding module 822, and/or other instruction modules.

Data obtaining module 808 may be configured to obtain scientific datafrom sci-data gathering devices. The sci-data gathering devices mayinclude bio-detection device configured to detect type of biologicalcells and/or substances proximate to the bio-detection device based onone or more images captured by the bio-detection device. A sci-datagathering device includes electronic instruments and equipment typicallyused in the gathering or observation of empirical data. This includesinstruments and equipment used in medical, laboratory, testing, andresearch facility. Examples include microscopes, scales, thermometers,glucose meters, etc. The smartdevice 220 and chip 301 are examples of abio-detection device.

Sci-data analysis module 810 may be configured to analyze gatheredsci-data to identify and categorize relevant scientific elements in thegathered sci-data. As used herein, a scientific element is a particularpiece of data or collection of associated data. The relevance may bedetermined by similarity to previous elements that were consideredrelevant. In addition, an AI/DL/ML engine may be used to determinerelevance based on a training and/or existing data.

Sci-data offering module 812 may be configured to offer the analyzedsci-data via a marketplace in exchange for a monetary value. Themarketplace may be implemented as e-commerce solution. Monetary valuerefers to a value based on some standard. For example, data may have amonetary value based on the U.S. dollar or the Euro.

Sci-data delivery module 814 may be configured to deliver the analyzedsci-data via the marketplace in exchange for an acceptable monetaryvalue. In some instance, a user of the marketplace may negotiate for thesci-data. Once there is a agree-upon value, an exchange takes place.

Sci-data associating module 816 may be configured to associate theobtained sci-data to an account. An account may be owed or connected toa person, company, or some legal entity.

Value determination module 818 may be configured to determine themonetary value of analyzed sci-data.

Value minting module 820 may be configured to mint cryptocurrency basedon the determined monetary value of the analyzed sci-data. Thedetermined monetary value may be based on previously determined valuesfor similar relevant scientific elements.

Cryptocurrency awarding module 822 may be configured to award the mintedcryptocurrency to the account associated with the obtained sci-data.

In some implementations, sci-data may include information collected viaobservation or experimentation using the scientific method which is thesystematic pursuit of knowledge involving the recognition andformulation of a problem and testing of hypotheses. In someimplementations, sci-data may include information categorized inaccordance with known scientific disciplines (e.g., biology, chemistry,etc.). In some implementations, sci-data may include biological data.

In some implementations, server(s) 802, client computing platform(s)804, and/or external resources 824 may be operatively linked via one ormore electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which server(s) 802, clientcomputing platform(s) 804, and/or external resources 824 may beoperatively linked via some other communication media.

A given client computing platform 804 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given client computing platform 804 to interface with system 800and/or external resources 824, and/or provide other functionalityattributed herein to client computing platform(s) 804. By way ofnon-limiting example, the given client computing platform 804 mayinclude one or more of a desktop computer, a laptop computer, a handheldcomputer, a tablet computing platform, a NetBook, a Smartphone, a gamingconsole, and/or other computing platforms.

External resources 824 may include sources of information outside ofsystem 800, external entities participating with system 800, and/orother resources. In some implementations, some or all of thefunctionality attributed herein to external resources 824 may beprovided by resources included in system 800.

Server(s) 802 may include electronic storage 826, one or more processors828, and/or other components. Server(s) 802 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 802 in FIG.8 is not intended to be limiting. Server(s) 802 may include a pluralityof hardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 802. Forexample, server(s) 802 may be implemented by a cloud of computingplatforms operating together as server(s) 802.

Electronic storage 826 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 826 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)802 and/or removable storage that is removably connectable to server(s)802 via, for example, a port (e.g., a USB port, a firewire port, etc.)or a drive (e.g., a disk drive, etc.). Electronic storage 826 mayinclude one or more of optically readable storage media (e.g., opticaldisks, etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EEPROM, RAM, etc.), solid-state storage media(e.g., flash drive, etc.), and/or other electronically readable storagemedia. Electronic storage 826 may include one or more virtual storageresources (e.g., cloud storage, a virtual private network, and/or othervirtual storage resources). Electronic storage 826 may store softwarealgorithms, information determined by processor(s) 828, informationreceived from server(s) 802, information received from client computingplatform(s) 804, and/or other information that enables server(s) 802 tofunction as described herein.

Processor(s) 828 may be configured to provide information processingcapabilities in server(s) 802. As such, processor(s) 828 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 828 is shown in FIG. 8 asa single entity, this is for illustrative purposes only. In someimplementations, processor(s) 828 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 828 may represent processing functionality of aplurality of devices operating in coordination. Processor(s) 828 may beconfigured to execute modules 808, 810, 812, 814, 816, 818, 820, and/or822, and/or other modules. Processor(s) 828 may be configured to executemodules 808, 810, 812, 814, 816, 818, 820, and/or 822, and/or othermodules by software; hardware; firmware; some combination of software,hardware, and/or firmware; and/or other mechanisms for configuringprocessing capabilities on processor(s) 828. As used herein, the term“module” may refer to any component or set of components that performthe functionality attributed to the module. This may include one or morephysical processors during execution of processor readable instructions,the processor readable instructions, circuitry, hardware, storage media,or any other components.

It should be appreciated that although modules 808, 810, 812, 814, 816,818, 820, and/or 822 are illustrated in FIG. 8 as being implementedwithin a single processing unit, in implementations in whichprocessor(s) 828 includes multiple processing units, one or more ofmodules 808, 810, 812, 814, 816, 818, 820, and/or 822 may be implementedremotely from the other modules. The description of the functionalityprovided by the different modules 808, 810, 812, 814, 816, 818, 820,and/or 822 described below is for illustrative purposes, and is notintended to be limiting, as any of modules 808, 810, 812, 814, 816, 818,820, and/or 822 may provide more or less functionality than isdescribed. For example, one or more of modules 808, 810, 812, 814, 816,818, 820, and/or 822 may be eliminated, and some or all of itsfunctionality may be provided by other ones of modules 808, 810, 812,814, 816, 818, 820, and/or 822. As another example, processor(s) 828 maybe configured to execute one or more additional modules that may performsome or all of the functionality attributed below to one of modules 808,810, 812, 814, 816, 818, 820, and/or 822.

FIGS. 9A and/or 9B illustrates a method 900 that facilitatesdistribution of scientific data, in accordance with one or moreimplementations. The operations of method 900 presented below areintended to be illustrative. In some implementations, method 900 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of method 900 are illustrated in FIGS.9A and/or 9B and described below is not intended to be limiting.

In some implementations, method 900 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 900 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 900.

FIG. 9A illustrates method 900, in accordance with one or moreimplementations.

An operation 902 may include obtaining scientific data from sci-datagathering devices. Operation 902 may be performed by one or morehardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to data obtainingmodule 808, in accordance with one or more implementations.

An operation 904 may include analyzing gathered sci-data to identify andcategorize relevant scientific elements in the gathered sci-data.Operation 904 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to sci-data analysis module 810, in accordancewith one or more implementations.

An operation 906 may include offering the analyzed sci-data via amarketplace in exchange for a monetary value. Operation 906 may beperformed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to sci-data offering module 812, in accordance with one or moreimplementations.

An operation 908 may include delivering the analyzed sci-data via themarketplace in exchange for an acceptable monetary value. Operation 908may be performed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to sci-data delivery module 814, in accordance with one or moreimplementations.

FIG. 9B illustrates method 900, in accordance with one or moreimplementations.

An operation 910 may include associating the obtained sci-data to anaccount. Operation 910 may be performed by one or more hardwareprocessors configured by machine-readable instructions including amodule that is the same as or similar to sci-data associating module816, in accordance with one or more implementations.

An operation 912 may include determining monetary value of analyzedsci-data. Operation 912 may be performed by one or more hardwareprocessors configured by machine-readable instructions including amodule that is the same as or similar to value determination module 818,in accordance with one or more implementations.

An operation 914 may include minting cryptocurrency based on thedetermined monetary value of the analyzed sci-data. Operation 914 may beperformed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to value minting module 820, in accordance with one or moreimplementations.

An operation 916 may include awarding the minted cryptocurrency to theaccount associated with the obtained sci-data. Operation 916 may beperformed by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to cryptocurrency awarding module 822, in accordance with one ormore implementations.

Glossary

The following is a list of relevant terms used herein. Unless thecontext in which the term is used indicates differently, the terms ofthis glossary may be understood as being described in this glossary inaccordance with the technology described herein.

Electronic Device: An apparatus that includes one or more electroniccomponents designed to control or regulate the flow of electricalcurrents for the purpose of information processing or system control. Anelectronic device may include some mechanical, optical, and/or otherwisenon-electronic parts in addition to its electronic components. Examplesof such electronic components include transistors, diodes, capacitors,integrated circuits, and the like. Often such devices have one or moreprocessors that are capable of executing instructions, memories,input/output mechanisms (e.g., display screens, touchscreens, cameras,etc.), and communication systems (e.g., wireless networking and cellulartelephony). Examples of an electronic device contemplated hereinincludes a smartphone, a tablet computer, medical equipment, amicroscope, a smartdevice, a computer, a standalone unit, a collectionof cooperative units, a button-sized unit, system-on-a-chip, a device ona chip, an accessory to a smartphone or smartdevice, an ambulatorydevice, a robot, swallowability device, an injectable device, or thelike. Depending on the implementation, the electronic device may becharacterized as: portable; handheld; fits into a typical pocket;lightweight; portable and with fixed (non-aimable) optics—thus, thedevice must be moved to aim the optics; with aimable optics—thus, thedevice need not be moved to aim the optics; or a combination thereof. Inaddition, an implementation of an electronic device may be characterizedas a smartdevice (e.g., smartphone or tablet computer) with its ownprocessor and camera (as its scene-capture system); an accessory or casefor a smartdevice that operatively attaches to the smartdevice and addsadditionally processing capabilities and functionalities for itsscene-capture system; stand-alone device with its own processor andcamera (as its scene-capture system); ambulatory device that can moveunder its own power; a device-on-a-chip; system-on-a-chip; or a wirelessdevice that is configured to interconnect with a wireless network ofsuch devices, this device has its own processor camera (as itsscene-capture system).

System: An assemblage or combination of things, parts, or componentsthat form a unitary or cooperative whole. In some instances, a systemand platform are used synonymously.

Scene: An area, place, location, scenario, etc. that is in view of thescene-capture system.

Image: An array (e.g., two-dimensional) of data derived from and mappedto a scene. An image may be an array of measured data regarding theelectromagnetic spectrum emanating from, reflected off, passing through,scattering off of, etc. the contents (e.g., matter) of the scene. Theimage has an inherent frame or bound around or surrounding the subjectscene.

In situ: Describes something that is situated in the original, natural,or existing place or position. Something that is in place or position.It is undisturbed.

In-the-field: A synonym for in situ.

In the lab: Describes the opposite of in situ. That is, it describessomething that has been removed from its original or natural place orposition. It is something that is not in place. It has beenrepositioned.

Biological cell: In biology, a cell is the basic structural, functional,and biological unit of all known living organisms. Typically, biologicalcells consist of cytoplasm enclosed within a membrane, which containsmany biomolecules such as proteins and nucleic acids. Organisms can beclassified as single-celled or unicellular (consisting of a single cell;including bacteria) or multicellular (such as plants and animals). Whilethe multicellular plants and animals are often visible to the unaidedhuman eye, their individual cells are visible only under a microscope,with dimensions between 1 and 100 micrometers.

Biological substance: As used herein, a biological substance is notitself a biological cell. Rather, this is a substance is stronglyassociated with biological cells or lifeforms. In particular, abiological substance maybe part of or produced by a biological cell orlifeform. In other instances, a biological substance is capable ofaffecting a lifeform (or some portion thereof).

Biological cells and/or substances: As used herein, this refers to both“biological cells” and “biological substances.”

Type or class of biological cell: The cells may be classified,categorized, or typed based on identifiable characteristics (e.g.,physical, chemical, behavioral, etc.). For example, some cells may beclassified as pathological because they cause disease. Some may be adiseased typed because they are malfunctioning and/or infected.

Micrographic: An image is classified as micrographic when it capturescontent that is on a microscopic scale. Such content includes thingsthat are less than 100 micrometers in size. More generally, it includesitems smaller than a macroscopic scale (which are visible to the unaidedhuman eye) and quantum scale (i.e., atomic and subatomic particles).

Spectrographic: An image is classified as spectrographic when itcaptures the interaction between matter and some portion of theelectromagnetic spectrum. Examples of such interactions includedabsorption, emission, scattering, reflection, refraction, translucency,etc.

Optical: Physics that involves the behavior and properties of light,including its interactions with matter and instruments that use ordetect it. However, optics involve more than just the visible spectrum.

Visible Spectrum: This is part of the spectrographic image butspecifically includes some portion of the visible spectrum (i.e., light)and excludes the non-visible portions.

Digital: This describes data that is formatted and arranged so as to bemanaged and stored by a machine, computer, digital electronic device, orthe like. A data in the form of a digital signal uses discrete steps totransfer information.

Disease: Any disordered or malfunctioning lifeform or some portionthereof. A diseased lifeform is still alive but ill, sick, ailing, orthe like.

Pathological: Something that is capable of causing disease ormalfunction in a lifeform (or a portion thereof). A pathogen ispathological.

Pathobiological: Something is pathobiological if it is either capable ofcausing the disease to a lifeform (or some portion thereof) or is adiseased lifeform (or some portion thereof).

Pathobiological cell: A biological cell that is pathobiological.

Pathobiological substance: This is a substance that is either capable ofcausing the disease to a lifeform (or some portion thereof) or isassociated with a diseased lifeform (or some portion thereof). Thesubstance is not itself a biological cell.

Pathobiological cells and/or substances: As used herein, the term“pathobiological” modifies both “cell” and “substance.”

Pathogen: A biological cell (e.g., unicellular organism) that is capableof causing a disease. More generally, anything that can cause or producedisease.

Diseased cell: A biological cell (e.g., cancerous cell) that is alivebut diseased.

Lifeform: The body form that characterizes an organism. Examples oflifeforms include:

Plants—Multicellular, photosynthetic eukaryotes

Animals—Multicellular, eukaryotic organisms

Fungus—Eukaryotic organisms that include microorganisms such as yeastsand molds

Protists—a Eukaryotic organism that is not an animal, plant or fungus

Archaea—Single-celled microorganisms

Bacteria—Prokaryotic microorganisms

Organism: An organism may generally be characterized as containingdifferent levels of the organization; utilizing energy; responding tostimuli/environment; maintaining homeostasis; undergoing metabolism;growing; reproduction; and adapting to its environment.

Environmental factor: It is anything measurable that is capable ofaffecting the scene or that is associated with the scene. Such thingscan be abiotic or biotic. Abiotic factors include, for example, ambienttemperature, moisture, humidity, radiation, the amount of sunlight, andpH of the water medium (e.g., soil) where a microbe lives. Examples ofbiotic factors include the availability of food organisms and thepresence of conspecifics, competitors, predators, and parasites.

Smartphone: Generally, this term refers to a portable electronic devicewith features that are useful for mobile communication and computingusage. Such features the ability to place and receive voice/video calls,create and receive text messages, an event calendar, a media player,video games, GPS navigation, digital camera and video camera.

Smartdevice: The concept of a smartdevice includes a smartphone, but italso includes any other portable electronic device that might not haveall of the features and functionality of a smartphone. Examples of asmartdevice include a tablet computer, portable digital assistant, smartwatches, fitness tracker, location trackers, a so-calledinternet-of-things device, and the like. A smartdevice is an electronicdevice that is generally connected to other devices or networks viadifferent wireless protocols such as Bluetooth, NFC, Wi-Fi, 3G, etc.,that can operate to some extent interactively and autonomously.

Accessory: As used herein, this is an accessory to an electronic device(such as a smartphone or smartdevice). It adds additional functionalityand/or capabilities to the electronic device. Examples of suchaccessories include a smartwatch or electronically enabled phone case.

Additional and Alternative Implementation Notes

In the above description of example implementations, for purposes ofexplanation, specific numbers, materials configurations, and otherdetails are set forth in order to better explain the present disclosure.However, it will be apparent to one skilled in the art that the subjectmatter of the claims may be practiced using different details than theexamples ones described herein. In other instances, well-known featuresare omitted or simplified to clarify the description of the exampleimplementations.

The terms “techniques” or “technologies” may refer to one or moredevices, apparatuses, systems, methods, articles of manufacture, and/orexecutable instructions as indicated by the context described herein.

As used in this application, the term “or” is intended to mean aninclusive “or” rather than an exclusive “or.” That is, unless specifiedotherwise or clear from context, “X employs A or B” is intended to meanany of the natural inclusive permutations. That is, if X employs A; Xemploys B; or X employs both A and B, then “X employs A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more,” unlessspecified otherwise or clear from context to be directed to a singularform.

These processes are illustrated as a collection of blocks in a logicalflow graph, which represents a sequence of operations that may beimplemented in mechanics alone, with hardware, and/or with hardware incombination with firmware or software. In the context ofsoftware/firmware, the blocks represent instructions stored on one ormore non-transitory computer-readable storage media that, when executedby one or more processors or controllers, perform the recitedoperations.

Note that the order in which the processes are described is not intendedto be construed as a limitation, and any number of the described processblocks can be combined in any order to implement the processes or analternate process. Additionally, individual blocks may be deleted fromthe processes without departing from the spirit and scope of the subjectmatter described herein.

The term “computer-readable media” is non-transitory computer-storagemedia or non-transitory computer-readable storage media. For example,computer-storage media or computer-readable storage media may include,but are not limited to, magnetic storage devices (e.g., hard disk,floppy disk, and magnetic strips), optical disks (e.g., compact disk(CD) and digital versatile disk (DVD)), smart cards, flash memorydevices (e.g., thumb drive, stick, key drive, and SD cards), andvolatile and non-volatile memory (e.g., random access memory (RAM),read-only memory (ROM)).

What is claimed is:
 1. A system configured that facilitates distributionof scientific data, the system comprising: one or more hardwareprocessors configured by machine-readable instructions to: obtainscientific data from sci-data gathering devices; analyze gatheredsci-data to identify and categorize relevant scientific elements in thegathered sci-data; offer the analyzed sci-data via a marketplace inexchange for a monetary value; and deliver the analyzed sci-data via themarketplace in exchange for an acceptable monetary value.
 2. The systemof claim 1, wherein the one or more hardware processors are furtherconfigured by machine-readable instructions to: associate the obtainedsci-data to an account; determine monetary value of analyzed sci-data;mint cryptocurrency based on the determined monetary value of theanalyzed sci-data; award the minted cryptocurrency to the accountassociated with the obtained sci-data.
 3. The system of claim 1, whereinsci-data includes information collected via observation orexperimentation using the scientific method which is the systematicpursuit of knowledge involving the recognition and formulation of aproblem and testing of hypotheses.
 4. The system of claim 1, whereinsci-data includes information categorized in accordance with knownscientific disciplines.
 5. The system of claim 1, wherein sci-dataincludes biological data.
 6. The system of claim 1, wherein the sci-datagathering devices include bio-detection device configured to detect typeof biological cells and/or substances proximate to the bio-detectiondevice based on one or more images captured by the bio-detection device.7. The system of claim 1, wherein the determined monetary value is basedon previously determined values for similar relevant scientificelements.
 8. A method that facilitates distribution of scientific data,the method comprising: obtaining scientific data from sci-data gatheringdevices; analyzing gathered sci-data to identify and categorize relevantscientific elements in the gathered sci-data; offering the analyzedsci-data via a marketplace in exchange for a monetary value; anddelivering the analyzed sci-data via the marketplace in exchange for anacceptable monetary value.
 9. The method of claim 8, further comprising:associating the obtained sci-data to an account; determining monetaryvalue of analyzed sci-data; minting cryptocurrency based on thedetermined monetary value of the analyzed sci-data; and awarding theminted cryptocurrency to the account associated with the obtainedsci-data.
 10. The method of claim 8, wherein sci-data includesinformation collected via observation or experimentation using thescientific method which is the systematic pursuit of knowledge involvingthe recognition and formulation of a problem and testing of hypotheses.11. The method of claim 8, wherein sci-data includes informationcategorized in accordance with known scientific disciplines.
 12. Themethod of claim 8, wherein sci-data includes biological data.
 13. Themethod of claim 8, wherein the sci-data gathering devices includebio-detection device configured to detect type of biological cellsand/or substances proximate to the bio-detection device based on one ormore images captured by the bio-detection device.
 14. The method ofclaim 8, wherein the determined monetary value is based on previouslydetermined values for similar relevant scientific elements.
 15. Anon-transient computer-readable storage medium having instructionsembodied thereon, the instructions being executable by one or moreprocessors to perform a method that facilitates distribution ofscientific data, the method comprising: obtaining scientific data fromsci-data gathering devices; analyzing gathered sci-data to identify andcategorize relevant scientific elements in the gathered sci-data;offering the analyzed sci-data via a marketplace in exchange for amonetary value; and delivering the analyzed sci-data via the marketplacein exchange for an acceptable monetary value.
 16. The computer-readablestorage medium of claim 15, wherein the method further comprises:associating the obtained sci-data to an account; determining monetaryvalue of analyzed sci-data; minting cryptocurrency based on thedetermined monetary value of the analyzed sci-data; and awarding theminted cryptocurrency to the account associated with the obtainedsci-data.
 17. The computer-readable storage medium of claim 15, whereinsci-data includes information collected via observation orexperimentation using the scientific method which is the systematicpursuit of knowledge involving the recognition and formulation of aproblem and testing of hypotheses.
 18. The computer-readable storagemedium of claim 15, wherein sci-data includes information categorized inaccordance with known scientific disciplines.
 19. The computer-readablestorage medium of claim 15, wherein sci-data includes biological data.20. The computer-readable storage medium of claim 15, wherein thesci-data gathering devices include bio-detection device configured todetect type of biological cells and/or substances proximate to thebio-detection device based on one or more images captured by thebio-detection device.